Estimation of missing judgments in AHP pairwise matrices using a neural network-based model

Gómez-Ruiz, José Antonio; Karanik, Marcelo; Peláez, José Ignacio

doi:10.1016/j.amc.2010.04.009

Cited by 36 publications

(17 citation statements)

References 13 publications

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“…Some of them use the incomplete matrix to assign priority weight to each alternative without completion [7,8,9,10,11]. Some methods are devoted to estimate the missing values in matrix [12,13,14,15,16,17,18,19,20,21,22]. While these methods have a good effect on allocating the priority weights to alternatives, the methods that focus on matrix completion are more "well-rounded" to restore the preference relations between each pair of alternatives, which are inaccessible before completion.…”

Section: Introductionmentioning

confidence: 99%

A DEMATEL-based completion method for incomplete pairwise comparison matrix in AHP

et al. 2018

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Pairwise comparison matrix as a crucial component of AHP, presents the preference relations among alternatives. However, in many cases, the pairwise comparison matrix is difficult to complete, which obstructs the subsequent operations of the classical AHP. In this paper, based on DEMATEL which has ability to derive the total relation matrix from direct relation matrix, a new completion method for incomplete pairwise comparison matrix is proposed. The proposed method provides a new perspective to estimate the missing values with explicit physical meaning. Besides, the proposed method has low computational cost. This promising method has a wide application in multi-criteria decision-making.

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Section: Introductionmentioning

confidence: 99%

A DEMATEL-based completion method for incomplete pairwise comparison matrix in AHP

et al. 2018

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“…If consistency is present, there are many derivation methods for priority vectors, including the eigenvector method (Saaty [36]), weighted least squares method (Chu, Kalaba, and Spingarn [37]), logarithmic least squares method (Crawford and Williams [38]), a heuristic approach (Lin et al [39]), and the cosine maximization method (Kou and Lin [40], etc). Moreover, Gomez-Ruiz [41] proposed a estimation method using neural network.…”

Section: Preliminariesmentioning

confidence: 99%

A Similarity Measure-based Optimization Model for Group Decision Making with Multiplicative and Fuzzy Preference Relations

Chao

Kou

2016

INT J COMPUT COMMUN

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Group decision making (GDM) problem based on different preference relations aims to obtain a collective opinion based on various preference structures provided by a group of decision makers (DMs) or experts, those who have varying backgrounds and interests in real world. The decision process in proposed question includes three steps: integrating varying preference structures, reaching consensus opinion, selecting the best alternative. Two major approaches: preference transformation and optimization methods have been developed to deal with the issue in first step. However, the transformation processes causes information lose and existing optimization methods are so computationally complex that it is not easy to be used by management practice. This study proposes a new consistency-based method to integrate multiplicative and fuzzy preference relations, which is based on a cosine similarity measure to derive a collective priority vector. The basic idea is that a collective priority vector should be as similar per column as possible to a pairwise comparative matrix (PCM) in order to assure the group preference has highest consistency for each decision makers. The model is computationally simple, because it can be solved using a Lagrangian approach and obtain a collective priority vector following four simple steps. The proposed method can further used to derive priority vector of fuzzy AHP. Using three illustrative examples, the effectiveness and simpleness of the proposed model is demonstrated by comparison with other methods. The results show that the proposed model achieves the largest cosine values in all three examples, indicating the solution is the nearest theoretical perfectly consistent opinion for each decision makers.

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“…Currently, there are many popular methods that overcomes the drawbacks of the traditional qualitative method [1][2][3], such as marginal cost analysis, AHP (Analytic Hierarchy Process), fuzzy logic, grey system theory, clustering analysis and neural network. Some studies combine multiple methods to push forward a comprehensive assessment in order to make up the flaws of single method in complicated engineering system design, such as fuzzy AHP [4], fuzzy clustering analysis [5], neural AHP [6], grey fuzzy analysis [7] and so forth. Among these methods, there are still some problems: 1) the travel utility is calculated simply by the product of total travel time and unit value of time, which does not make sense in some cases; 2) the estimation of weight factors rely on the expert's score, which makes it difficult to eliminate the influence from personal subjectivity and uncertainty.…”

Section: Literature Reviewmentioning

confidence: 99%

Assessment on the Cohesion Pattern of Rail Transit: Integration of Rough Set and Grey Correlation Theory

2016

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Abstract. Most existing project assessment relies on expert scoring, whose precision can be deteriorated by personal subjectivity. This paper presents an assessment method to compare the advantages and disadvantages of three cohesion patterns between suburban and urban rail transit network, which reduces the influence of subjective score. A modified rough set -grey correlation model is developed as a core of this assessment, based on the integration of rough set theory and grey correlational analysis, where an index system is set up for model calculation. A case study using the network in Ningbo is applied to demonstrate the effectiveness of the method, the results show that the method is more effective using discretely distributed data sensitive to sample size. The consistency of the results in comparison with marginal cost analysis can be a preliminary verification of the model.

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Estimation of missing judgments in AHP pairwise matrices using a neural network-based model

Cited by 36 publications

References 13 publications

A DEMATEL-based completion method for incomplete pairwise comparison matrix in AHP

A DEMATEL-based completion method for incomplete pairwise comparison matrix in AHP

A Similarity Measure-based Optimization Model for Group Decision Making with Multiplicative and Fuzzy Preference Relations

Assessment on the Cohesion Pattern of Rail Transit: Integration of Rough Set and Grey Correlation Theory

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